hiera_abswin_base_mim-intermediate-eu-common

Model Card for hiera_abswin_base_mim-intermediate-eu-common

A Hiera image classification model. The model follows a three-stage training process: first, masked image modeling, next intermediate training on a large-scale dataset containing diverse bird species from around the world, finally fine-tuned specifically on the eu-common dataset.

The species list is derived from the Collins bird guide ¹.

Model Details

• Model Type: Image classification and detection backbone

• Model Stats:

  • Params (M): 51.1
  • Input image size: 384 x 384

• Dataset: eu-common (707 classes)

  • Intermediate training involved ~6000 species from asia, europe and africa

• Papers:

  • Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles: https://arxiv.org/abs/2306.00989
  • Window Attention is Bugged: How not to Interpolate Position Embeddings: https://arxiv.org/abs/2311.05613

Model Usage

Image Classification

import birder
from birder.inference.classification import infer_image

(net, model_info) = birder.load_pretrained_model("hiera_abswin_base_mim-intermediate-eu-common", inference=True)

# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)

# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)

image = "path/to/image.jpeg"  # or a PIL image, must be loaded in RGB format
(out, _) = infer_image(net, image, transform)
# out is a NumPy array with shape of (1, 707), representing class probabilities.

Image Embeddings

import birder
from birder.inference.classification import infer_image

(net, model_info) = birder.load_pretrained_model("hiera_abswin_base_mim-intermediate-eu-common", inference=True)

# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)

# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)

image = "path/to/image.jpeg"  # or a PIL image
(out, embedding) = infer_image(net, image, transform, return_embedding=True)
# embedding is a NumPy array with shape of (1, 768)

Detection Feature Map

from PIL import Image
import birder

(net, model_info) = birder.load_pretrained_model("hiera_abswin_base_mim-intermediate-eu-common", inference=True)

# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)

# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)

image = Image.open("path/to/image.jpeg")
features = net.detection_features(transform(image).unsqueeze(0))
# features is a dict (stage name -> torch.Tensor)
print([(k, v.size()) for k, v in features.items()])
# Output example:
# [('stage1', torch.Size([1, 96, 96, 96])),
#  ('stage2', torch.Size([1, 192, 48, 48])),
#  ('stage3', torch.Size([1, 384, 24, 24])),
#  ('stage4', torch.Size([1, 768, 12, 12]))]

Citation

@misc{ryali2023hierahierarchicalvisiontransformer,
      title={Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles},
      author={Chaitanya Ryali and Yuan-Ting Hu and Daniel Bolya and Chen Wei and Haoqi Fan and Po-Yao Huang and Vaibhav Aggarwal and Arkabandhu Chowdhury and Omid Poursaeed and Judy Hoffman and Jitendra Malik and Yanghao Li and Christoph Feichtenhofer},
      year={2023},
      eprint={2306.00989},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2306.00989},
}

@misc{bolya2023windowattentionbuggedinterpolate,
      title={Window Attention is Bugged: How not to Interpolate Position Embeddings},
      author={Daniel Bolya and Chaitanya Ryali and Judy Hoffman and Christoph Feichtenhofer},
      year={2023},
      eprint={2311.05613},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2311.05613},
}

Footnotes

1. Svensson, L., Mullarney, K., & Zetterström, D. (2022). Collins bird guide (3rd ed.). London, England: William Collins. ↩